Thomas A. Drake

Genetics of gene expression surveyed in maize, mouse and man

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.

Mapping the Genetic Architecture of Gene Expression in Human Liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

An integrative genomics approach to infer causal associations between gene expression and disease

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene–perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

Variations in DNA elucidate molecular networks that cause disease

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase β (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Genetic Control of Obesity and Gut Microbiota Composition in Response to High-Fat, High-Sucrose Diet in Mice

Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity.

Oxygenated Carotenoid Lutein and Progression of Early Atherosclerosis The Los Angeles Atherosclerosis Study

Background—Carotenoids are hypothesized to explain some of the protective effects of fruit and vegetable intake on risk of cardiovascular disease. The present study assessed the protective effects of the oxygenated carotenoid lutein against early atherosclerosis. Methods and Results—Epidemiology: Progression of intima-media thickness (IMT) of the common carotid arteries over 18 months was determined ultrasonographically and was related to plasma lutein among a randomly sampled cohort of utility employees age 40 to 60 years (n=480). Coculture: The impact of lutein on monocyte response to artery wall cell modification of LDL was assessed in vitro by quantification of monocyte migration in a coculture model of human intima. Mouse models: The impact of lutein supplementation on atherosclerotic lesion formation was assessed in vivo by assigning apoE-null mice to chow or chow plus lutein (0.2% by weight) and LDL receptor-null mice to Western diet or Western diet plus lutein. IMT progression declined with increasing quintile of plasma lutein (P for trend=0.007, age-adjusted;P =0.0007, multivariate). Covariate-adjusted IMT progression (mean±SEM) was 0.021±0.005 mm in the lowest quintile of plasma lutein, whereas progression was blocked in the highest quintile (0.004±0.005 mm;P =0.01). In the coculture, pretreatment of cells with lutein inhibited LDL-induced migration in a dose-dependent manner (P <0.05). Finally, in the mouse models, lutein supplementation reduced lesion size 44% in apoE-null mice (P =0.009) and 43% in LDL receptor-null mice (P =0.02). Conclusions—These epidemiological, in vitro, and mouse model findings support the hypothesis that increased dietary intake of lutein is protective against the development of early atherosclerosis.

Pathology of atheromatous lesions in inbred and genetically engineered mice. Genetic determination of arterial calcification.

We report comprehensive pathological studies of atheromatous lesions in various inbred mouse strains fed a high-fat, high-cholesterol diet and in two genetically engineered strains that develop spontaneous lesions on a low-fat chow diet. Coronary and aortic lesions were studied with respect to anatomic locations, lesion severity, calcification, and lipofuscin deposition. Surprisingly, the genetic determinants for coronary fatty lesion formation differed in part from those for aortic lesion development. This suggests the existence of genetic factors acting locally as well as systematically in lesion development. We used immunohistochemical analyses to determine the cellular and molecular compositions of the lesions. The aortic lesions contained monocyte/macrophages, lipid, apolipoprotein B, serum amyloid A proteins, and immunoglobulin M and showed expression of vascular cell adhesion molecule-1 and tumor necrosis factor-alpha, all absent in normal arteries. In certain strains, advanced lesions developed in which smooth muscle cells were commonly observed. The lesions in mice targeted for a null mutation in the apolipoprotein E gene were much larger, more widely dispersed, and more fibrous, cellular, and calcified in nature than the lesions in laboratory inbred strains. When apolipoprotein A-II transgenic mice were maintained on a low-fat chow diet, the lesions in these mice were relatively small and located in the very proximal regions of the aorta. There were clear differences in the occurrence of arterial wall calcification among genetically distinct inbred mouse strains, indicating for the first time a genetic component in this clinically significant trait. Analysis of a genetic cross indicated a complex pattern of calcification inheritance with incomplete penetrance.

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm.